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Lidar is the most important sensor for roadside perception in autonomous driving and the Connected Automated Vehicle Highway(CAVH). ...Secondly, the performance of roadside Lidar perception algorithm in different weather types is analyzed. Different from the traditional way of signal processing, this paper introduces deep neural network and realizes the classification of different weather types.

This paper aims to elucidate the development and validation of a phenomenological LIDAR sensor model, as well as its utilization in the development of sensor fusion algorithms. By leveraging this approach, researchers can effectively simulate sensor behavior, facilitate faster development cycles, and enhance algorithmic advancements in autonomous systems.

A review of studies and data indicates this may have been the first-ever application of lidar on a traffic signal. Additional lidar sensors were placed at crossing signs and intersections in Reno, the nearby Tahoe Reno Industrial Center and in the city of Henderson, Nevada. ...As part of its research in transportation infrastructure, the University of Nevada, Reno’s Nevada Center for Applied Research, in conjunction with the Regional Transportation Commission of Washoe County and the Nevada DOT, used lidar sensors to collect data aimed at making transportation more efficient, sustainable and safe. ...The program integrated lidar sensors with traffic signals to detect, count and track pedestrians, cyclists and traffic.

With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed.

Recent developments in the area of autonomous vehicle navigation have emphasized algorithm development for the characterization of LiDAR 3D point-cloud data. The LiDAR sensor data provides a detailed understanding of the environment surrounding the vehicle for safe navigation. ...However, LiDAR point cloud datasets need point-level labels which require a significant amount of annotation effort. ...The simulated LiDAR data was generated by a physics-based platform, the Mississippi State University Autonomous Vehicle Simulator (MAVS).

The current approaches to achieve it are mainly using stereo vision, Structure from Motion (SfM) or mobile LiDAR sensors. Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. ...Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. ...This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. The key novelty of the method is the semantic coupling of stereoscopic point cloud with color lattice from camera image labelled through a Convolutional Neural Network (CNN).

In recent years, vehicle-intelligent road cooperation is gaining an increasing attention from both academia and industry, which require deployment of a large scale of road side sensors such as lidar and camera. For the road side sensors, calibration is indispensable to obtain transformation between sensor coordinate frame and geographic coordinate frame. ...To simplify the calibration task and improve efficiency, an automatic calibration method for road side lidar and camera using a planar calibration target is proposed in this paper. The feature of planar target is designed to be easily identified by the sensors, and an Integrated Navigation System (INS), which acquires the geographic coordinate of itself in real time at an accuracy of centimeter level, is fixed on the target. ...The experiment results show that the proposed calibration method is able to calibrate road side lidar and camera efficiently and accurately.

The main research content of this paper is to design a positioning algorithm for unmanned terminal Automated Guided Vehicle (AGV) based on single-line lidar, including point cloud data acquisition, background filtering, point cloud clustering, vehicle position extraction, and result optimization.

Collision avoidance in automated guided systems using a light detection and ranging (LIDAR) scanner has been studied for application in low-speed autonomous Honda Power Equipment products, such as self-driving lawn mowers. ...The automotive application of a LIDAR scanner for autonomous driving is used for obstacle detection and offline local area. Such delineations do not exist in areas where power equipment is used, such as grass fields; therefore, identifying object height and distance is a relatively new area. ...For this study, a small LIDAR scanner with a resolution of 0.01 m and a measurement range of 0.05 to 40.00 m was used on a Honda self-driving lawn mower.

The SORA-P60 uses Cepton’s Micro-Motion Technology (MMT) to achieve an industry leading 1,200 scan lines per second, to provide accurate 3D scans and to enable automated classification of objects and volumetric scanning. The sensor is currently installed at an undisclosed highway tolling facility in the United States.

The new entity – established in the apex of the U.S. automotive will give Ibeo Automotive Systems the ability to expand its business segment into the growing U.S. automated and connected vehicle market.

Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. ...One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. ...This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles.

This method utilizes physics-based simulation of sensors, along with automated truth labeling, to improve the speed and accuracy of training data acquisition for both camera and LIDAR sensors. This framework is enabled by the MSU Autonomous Vehicle Simulator (MAVS), a physics-based sensor simulator for ground vehicle robotics that includes high-fidelity simulations of LIDAR, cameras, and other sensors. ...This framework is enabled by the MSU Autonomous Vehicle Simulator (MAVS), a physics-based sensor simulator for ground vehicle robotics that includes high-fidelity simulations of LIDAR, cameras, and other sensors.

To tackle this challenge, this study proposes a pedestrian detection algorithm based on roadside Light Detection And Ranging (LiDAR) by combining traditional and deep learning algorithms. To meet real-time demand, Octree with region-of-interest (ROI) selection is introduced and improved to filter the background in each frame, which improves the clustering speed. ...Afterward, an improved Euclidean clustering algorithm was proposed by analyzing the scanning characteristics of LiDAR. Concretely, on account of the vertical and the horizontal angular resolution of the LiDAR, the authors propose a new method for determining the search radius of Euclidean clustering with adaptive distance. ...What’s more, the entire background filtering and clustering process takes 88.7 ms per frame, and the model obtained was deployed on NVIDIA Jetson AGX Xavier, attaining the inference time of 110 ms per frame, which can meet the speed requirement of LiDAR update and achieve the real-time application.

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers.

However, much of the previous research has predominantly focused on the fusion of Lidar and cameras. Although Lidar offers an ample supply of point cloud data, its high cost and the substantial volume of point cloud data can lead to computational delays.

The application of lidar in automated vehicles has also become more and more popular, and algorithms for point cloud object detection have emerged endlessly. ...In addition, the algorithm accomplishes object detection in 69 ms, which can meet the speed requirement of lidar update and achieve the real-time application.

Olsen of the Naval Postgraduate School offers an eclectic description of the technologies and underlying physics for a wide range of remote sensing systems, including optical, thermal, radar, and lidar systems. This monograph describes this diverse set of applications using full-color graphics and a friendly, readable format.

This paper discusses an approach of utilizing satellite images to estimate the drivable areas of agriculture fields with the aid of LiDAR sensor data to provide the necessary information for the vehicle to navigate autonomously. ...The images are used to detect the field boundaries while the LiDAR sensor detects the obstacles that the vehicle encounters during the autonomous driving as well as its type.

This paper uses three-dimensional Light Detection And Ranging (3D LiDAR) to efficiently search parking slots around without passing by them and highlights the accuracy of detecting and tracking. ...During this process, given LiDAR’s blind spots and visual field occlusion, it is necessary to select the most reliable corner as a positioning reference in each step to update the relative position of the ego-vehicle and slot.